Performance-balanced Loss.
Imbalance is also a problem in object detection where the foreground-background imbalance is extreme and undermines learning. Online Hard Example Mining (OHEM) [1]} proposes to find hard examples by ranking the losses and only keeping those with the highest losses. Focal loss [2]} chooses to down weight easy samples and emphasizes hard samples by weighting each sample by \(1-p\)  where \(p\)  is the predicted probability for the sample. The weight for each sample dynamically changes with training and the method never completely discards any samples. Focal loss is especially successful because it is easy to implement and proves effective in the binary foreground-background imbalance setting. However, it has not been very successful in other applications for dealing with imbalance as reported by [3]}.
